import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
                       'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

print(train_images.shape)
print(len(train_labels))
print(test_images.shape)

#plt.figure()
#plt.imshow(train_images[0])
#plt.colorbar()
#plt.gca().grid(False)

# why do this
train_images = train_images / 255.0
test_images = test_images / 255.0



#plt.figure(figsize=(10,10))
#for i in range(25):
#    plt.subplot(5,5,i+1)
#    plt.xticks([])
#    plt.yticks([])
#    plt.grid('off')
#    plt.imshow(train_images[i], cmap=plt.cm.binary)
#    plt.xlabel(class_names[train_labels[i]])
#

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])


# other loss, metrics and difference
model.compile(optimizer=tf.train.AdamOptimizer(), 
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
predictions = model.predict(test_images)
print(np.argmax(predictions[0]))

#plt.figure(figsize=(10,10))
#for i in range(25):
#    plt.subplot(5,5,i+1)
#    plt.xticks([])
#    plt.yticks([])
#    plt.grid('off')
#    plt.imshow(test_images[i], cmap=plt.cm.binary)
#    predicted_label = np.argmax(predictions[i])
#    true_label = test_labels[i]
#    if predicted_label == true_label:
#      color = 'green'
#    else:
#      color = 'red'
#    plt.xlabel("{} ({})".format(class_names[predicted_label], 
#                                  class_names[true_label]),
#                                  color=color)
#      



